Stakeholders and product teams today want you to deliver software faster than ever. Not to forget – delivery efficiency becomes even more crucial as you consistently update and offer new features. Amidst the dynamic development landscape, test automation is no longer a ‘nice-to-have’ aspect; it has become a necessity. However, to make your tests more effective, automating them with minor changes in test data won’t help you. The road to redemption? Data-driven testing – not only accelerates repeated tests and makes them more efficient but also makes life easier for testers and developers by separating test logic from test data.
Want to know more in detail? Stick around as we discuss data-driven testing – what it means, why it is critical, and how you can get started.
A number of tests involve repeating the same test case but with different data sets. Data-driven testing helps simplify the process by reading input data from test files rather than manual entries for each test. Hence, test data is separated from the test functionality. The entire test design and execution rely on data, and the test environment and settings are not hard coded in this approach.
For example – consider testing candidate details in a job application form. If you are hiring for multiple profiles, you need to be able to test a variety of educational backgrounds, experience durations, and other critical details. You don’t want to build one test case each for the candidate entries and put in hours or even days of monotonous work. So the optimal way is to parameterize one test case and run it with varying data points. This approach significantly amplifies the test coverage and assists the testing team in boosting their productivity.
The data-driven testing methodology involves a test passing all the different permutations from an external collection of data points and in various test environments. The inputs can be in the form of an Excel sheet, MySQL database, an XML file, and more. Another significant advantage is setting up positive and negative test cases into a single test. Positive test cases include the data points that reside inside the boundary and can be termed valid inputs. In contrast, negative test cases are outside the boundary and should generate invalid value errors.
Simply put, data-driven testing is testing the application logic on auto-pilot. From a top-level view, this methodology’s ease of handling errors, high efficiency, and time-saving aspects stand out among the benefits. Let’s look at the top reasons why you should use DDT for effective testing.
- Parting the test logic from test data makes the process highly organized and effective. Both the inputs and outputs can be stored systematically. This decoupling helps keep test and data as separate entities that can be utilized, updated, and repurposed without any dependency.
- Recreation of tests is not required to add or modify the test input data as DDT utilizes external data sources.
- We all know how helpful increased test coverage is in software development. Since a single test is created in DDT and runs as often as required by varying the input data, the coverage is much improved.
- Only a single test is needed in the case of data-driven testing. Hence redundancy is cleared out, and iterative tests can be efficiently accomplished. That further means development and testing teams can be more productive with their time and efforts.
- Output validation can be performed at scale as the expected outcomes are also included in the input test data. The obtained output is compared effectively, and the results are generated quickly.
- Test scripting can be done during the software’s development time, not necessarily after. Input data source files can be created anytime – even before the development begins so that the testing becomes truly independent.
Wherever the same test logic is required with different test data, data-driven testing can be utilized. Let’s look at some of the most common use cases where DDT can be leveraged to optimize software delivery.
Your user registration process should be validated comprehensively before shipping. While signing up, details like email id, phone no., and other domain-specific ones need to be tested with a set of positive and negative data points. Not to forget, whenever users edit or update their information, the changes must also be tested.
User permissions are another major aspect to be tested. Remember the ‘allow location access?’ popups on various websites? You need to validate with various test cases whether the permissions are being given successfully and whether the process is getting updated.
Flight or other travel ticket booking systems involve complex logic. The source, destination, dates, ticket category, taxes, and other parameters vary widely. You need to test deeply if the system is calculating the correct cost. Several permutations and combinations should be considered while preparing input test data. Additionally, offers and discounts are never out of the equation, and the logic needs to be thoroughly validated for correct outputs and to prevent business loss.
Time difference and language are not the only location-specific variations. Most websites are expected to cater to different regions, languages, and generic cultural practices. For example, a thousand and a half dollars is $1,000.50 in the US and UK; however, in Germany and France, it’s 1.000,50$. More parameters like address and telephone formats, abbreviations, pricing, and currency differ with locations and must be addressed while testing. And the best way to not miss any test case is through data-driven testing.
DDT is similar to test automation, but we have already been talking about a big difference – the test data is not coupled with test logic, nor it’s ‘hard coded.’ Alternatively, you link your test procedure to a data source. The data source formats can be CSV files, XML documents, and robust databases like MySQL. Here’s a step-by-step approach to implementing data-driven testing like a pro:
First, analyze your use case. If you want to test and validate email addresses and password tuples, a simple CSV file will do the job. On the other hand, tests involving more in-depth information may need XML to fit in all the extra details. And huge, multi-dimensional test pools with end-to-end automation might require an entire MySQL database, which effectively organizes your tests.
While preparing your test input file, ensure you include all the possible permutations and keep an even distribution of positive, negative, and boundary value data points.
Now that your data source is ready, it’s time to connect it with the test procedure. An easy yet effective approach to do this is through Selenium. Take a look at how you can execute it:
- Import Your Test Data from CSV and run a loop that traverses through each entry in the input data source.
- In case of a complex test case, use an XML data source, import this to your test procedure, and parse it to generate outputs and validate the results. Generally, developers store the retrieved results and then compare them with the expected output.
With constantly improving software systems, test outcomes are not limited to having a binary scope. Figuring out how to analyze the outcomes gets a bit tricky. The ideal way is to utilize a test result statement, enabling you to equate the actual result against the expected result. If the obtained outcomes vary significantly, you can switch to XML for a brief description of the expected results.
Any testing methodology that is not data-driven involves a sample dataset within the test. On the other hand, a data-driven test procedure will link to an external source for data extraction for testing purposes. Data is the key in this testing mode; hence, the selection and preparation need maximum focus.
DDT is essentially a four-step process as follows:
- Preparing the input data points and expected outcomes in an organized file or a database.
- Building test procedure to parse/read the input data from the data source and running the test on a set of input values.
- Storing the generated results and comparing them with the expected output data, which depends on the stored external data
- Iterating the same steps and storing test results for the next set of data points but with the same test procedure.
Data-driven testing is a productive testing approach as it boosts test coverage, cuts down mundane tasks by reducing redundancies, and runs tests at scale without a single line of code. Modern software use cases usually require a large number of data points for efficient testing. However, getting an effective set of test data points can be overwhelming, especially if you’re just getting started with this approach.
Testsigma simplifies data-driven testing and makes test automation seamless for your software delivery.